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  <h1>Source code for torch.nn.quantized.functional</h1><div class="highlight"><pre>
<span></span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Functional interface (quantized).&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch._jit_internal</span> <span class="kn">import</span> <span class="n">List</span> <span class="k">as</span> <span class="n">_List</span>
<span class="kn">from</span> <span class="nn">torch.nn.modules.utils</span> <span class="kn">import</span> <span class="n">_pair</span><span class="p">,</span> <span class="n">_triple</span>

<span class="c1"># Although some of the functions and docstrings are mirrored from the torch.nn,</span>
<span class="c1"># we want to have them here for future changes.</span>

<div class="viewcode-block" id="avg_pool2d"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.avg_pool2d">[docs]</a><span class="k">def</span> <span class="nf">avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
               <span class="n">count_include_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">divisor_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size</span>
<span class="sd">    :math:`sH \times sW` steps. The number of output features is equal to the number of</span>
<span class="sd">    input planes.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    See :class:`~torch.nn.quantized.AvgPool2d` for details and output shape.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`</span>
<span class="sd">        kernel_size: size of the pooling region. Can be a single number or a</span>
<span class="sd">          tuple `(kH, kW)`</span>
<span class="sd">        stride: stride of the pooling operation. Can be a single number or a</span>
<span class="sd">          tuple `(sH, sW)`. Default: :attr:`kernel_size`</span>
<span class="sd">        padding: implicit zero paddings on both sides of the input. Can be a</span>
<span class="sd">          single number or a tuple `(padH, padW)`. Default: 0</span>
<span class="sd">        ceil_mode: when True, will use `ceil` instead of `floor` in the formula</span>
<span class="sd">            to compute the output shape. Default: ``False``</span>
<span class="sd">        count_include_pad: when True, will include the zero-padding in the</span>
<span class="sd">            averaging calculation. Default: ``True``</span>
<span class="sd">        divisor_override: if specified, it will be used as divisor, otherwise</span>
<span class="sd">             size of the pooling region will be used. Default: None</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.avg_pool2d&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
                                          <span class="n">ceil_mode</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">,</span>
                                          <span class="n">divisor_override</span><span class="p">)</span></div>

<span class="k">def</span> <span class="nf">avg_pool3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
               <span class="n">count_include_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">divisor_override</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies 3D average-pooling operation in :math:`kD \ times kH \times kW` regions by step size</span>
<span class="sd">    :math:`sD \times sH \times sW` steps. The number of output features is equal to the number of</span>
<span class="sd">    input planes.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`</span>
<span class="sd">        kernel_size: size of the pooling region. Can be a single number or a</span>
<span class="sd">          tuple `(kD, kH, kW)`</span>
<span class="sd">        stride: stride of the pooling operation. Can be a single number or a</span>
<span class="sd">          tuple `(sD, sH, sW)`. Default: :attr:`kernel_size`</span>
<span class="sd">        padding: implicit zero paddings on both sides of the input. Can be a</span>
<span class="sd">          single number or a tuple `(padD, padH, padW)`. Default: 0</span>
<span class="sd">        ceil_mode: when True, will use `ceil` instead of `floor` in the formula</span>
<span class="sd">            to compute the output shape. Default: ``False``</span>
<span class="sd">        count_include_pad: when True, will include the zero-padding in the</span>
<span class="sd">            averaging calculation. Default: ``True``</span>
<span class="sd">        divisor_override: if specified, it will be used as divisor, otherwise</span>
<span class="sd">             size of the pooling region will be used. Default: None</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.avg_pool3d&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">avg_pool3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
                                          <span class="n">ceil_mode</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">,</span>
                                          <span class="n">divisor_override</span><span class="p">)</span>

<div class="viewcode-block" id="adaptive_avg_pool2d"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.adaptive_avg_pool2d">[docs]</a><span class="k">def</span> <span class="nf">adaptive_avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, BroadcastingList2[int]) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies a 2D adaptive average pooling over a quantized input signal composed</span>
<span class="sd">    of several quantized input planes.</span>

<span class="sd">    .. note:: The input quantization paramteres propagate to the output.</span>

<span class="sd">    See :class:`~torch.nn.quantized.AdaptiveAvgPool2d` for details and output shape.</span>

<span class="sd">    Args:</span>
<span class="sd">        output_size: the target output size (single integer or</span>
<span class="sd">                     double-integer tuple)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.adaptive_avg_pool2d&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">adaptive_avg_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span></div>

<div class="viewcode-block" id="conv2d"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.conv2d">[docs]</a><span class="k">def</span> <span class="nf">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span>
           <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
           <span class="n">padding_mode</span><span class="o">=</span><span class="s1">&#39;zeros&#39;</span><span class="p">,</span>
           <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
           <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies a 2D convolution over a quantized 2D input composed of several input</span>
<span class="sd">    planes.</span>

<span class="sd">    See :class:`~torch.nn.quantized.Conv2d` for details and output shape.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`</span>
<span class="sd">        weight: quantized filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)`</span>
<span class="sd">        bias: **non-quantized** bias tensor of shape :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.</span>
<span class="sd">        stride: the stride of the convolving kernel. Can be a single number or a</span>
<span class="sd">          tuple `(sH, sW)`. Default: 1</span>
<span class="sd">        padding: implicit paddings on both sides of the input. Can be a</span>
<span class="sd">          single number or a tuple `(padH, padW)`. Default: 0</span>
<span class="sd">        dilation: the spacing between kernel elements. Can be a single number or</span>
<span class="sd">          a tuple `(dH, dW)`. Default: 1</span>
<span class="sd">        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the</span>
<span class="sd">          number of groups. Default: 1</span>
<span class="sd">        padding_mode: the padding mode to use. Only &quot;zeros&quot; is supported for quantized convolution at the moment. Default: &quot;zeros&quot;</span>
<span class="sd">        scale: quantization scale for the output. Default: 1.0</span>
<span class="sd">        zero_point: quantization zero_point for the output. Default: 0</span>
<span class="sd">        dtype: quantization data type to use. Default: ``torch.quint8``</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; from torch.nn.quantized import functional as qF</span>
<span class="sd">        &gt;&gt;&gt; filters = torch.randn(8, 4, 3, 3, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt; inputs = torch.randn(1, 4, 5, 5, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt; bias = torch.randn(8, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; scale, zero_point = 1.0, 0</span>
<span class="sd">        &gt;&gt;&gt; dtype_inputs = torch.quint8</span>
<span class="sd">        &gt;&gt;&gt; dtype_filters = torch.qint8</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)</span>
<span class="sd">        &gt;&gt;&gt; q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)</span>
<span class="sd">        &gt;&gt;&gt; qF.conv2d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)</span>
<span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
    <span class="k">if</span> <span class="n">padding_mode</span> <span class="o">!=</span> <span class="s1">&#39;zeros&#39;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only zero-padding is supported!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only torch.quint8 is supported for activation tensor!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only torch.qint8 is supported for weight tensor!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input shape must be `(N, C, H, W)`!&quot;</span><span class="p">)</span>
    <span class="n">stride</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span>
    <span class="n">padding</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
    <span class="n">dilation</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">dilation</span><span class="p">)</span>

    <span class="n">prepacked_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">conv2d_prepack</span><span class="p">(</span>
        <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span>
                                      <span class="n">prepacked_weight</span><span class="p">,</span>
                                      <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span>
                                      <span class="n">groups</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">)</span></div>

<div class="viewcode-block" id="conv3d"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.conv3d">[docs]</a><span class="k">def</span> <span class="nf">conv3d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
           <span class="n">padding_mode</span><span class="o">=</span><span class="s1">&#39;zeros&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies a 3D convolution over a quantized 3D input composed of several input</span>
<span class="sd">    planes.</span>

<span class="sd">    See :class:`~torch.nn.quantized.Conv3d` for details and output shape.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input tensor of shape</span>
<span class="sd">          :math:`(\text{minibatch} , \text{in\_channels} , iD , iH , iW)`</span>
<span class="sd">        weight: quantized filters of shape</span>
<span class="sd">          :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)`</span>
<span class="sd">        bias: **non-quantized** bias tensor of shape</span>
<span class="sd">          :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.</span>
<span class="sd">        stride: the stride of the convolving kernel. Can be a single number or a</span>
<span class="sd">          tuple `(sD, sH, sW)`. Default: 1</span>
<span class="sd">        padding: implicit paddings on both sides of the input. Can be a</span>
<span class="sd">          single number or a tuple `(padD, padH, padW)`. Default: 0</span>
<span class="sd">        dilation: the spacing between kernel elements. Can be a single number or</span>
<span class="sd">          a tuple `(dD, dH, dW)`. Default: 1</span>
<span class="sd">        groups: split input into groups, :math:`\text{in\_channels}` should be</span>
<span class="sd">          divisible by the number of groups. Default: 1</span>
<span class="sd">        padding_mode: the padding mode to use. Only &quot;zeros&quot; is supported for</span>
<span class="sd">          quantized convolution at the moment. Default: &quot;zeros&quot;</span>
<span class="sd">        scale: quantization scale for the output. Default: 1.0</span>
<span class="sd">        zero_point: quantization zero_point for the output. Default: 0</span>
<span class="sd">        dtype: quantization data type to use. Default: ``torch.quint8``</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; from torch.nn.quantized import functional as qF</span>
<span class="sd">        &gt;&gt;&gt; filters = torch.randn(8, 4, 3, 3, 3, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt; inputs = torch.randn(1, 4, 5, 5, 5, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt; bias = torch.randn(8, dtype=torch.float)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; scale, zero_point = 1.0, 0</span>
<span class="sd">        &gt;&gt;&gt; dtype_inputs = torch.quint8</span>
<span class="sd">        &gt;&gt;&gt; dtype_filters = torch.qint8</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)</span>
<span class="sd">        &gt;&gt;&gt; q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)</span>
<span class="sd">        &gt;&gt;&gt; qF.conv3d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)</span>
<span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
    <span class="k">if</span> <span class="n">padding_mode</span> <span class="o">!=</span> <span class="s1">&#39;zeros&#39;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only zero-padding is supported!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only torch.quint8 is supported for activation tensor!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Only torch.qint8 is supported for weight tensor!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input shape must be `(N, C, D, H, W)`!&quot;</span><span class="p">)</span>
    <span class="n">stride</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">stride</span><span class="p">)</span>
    <span class="n">padding</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
    <span class="n">dilation</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">dilation</span><span class="p">)</span>

    <span class="n">prepacked_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">conv3d_prepack</span><span class="p">(</span>
        <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">conv3d</span><span class="p">(</span>
        <span class="nb">input</span><span class="p">,</span> <span class="n">prepacked_weight</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span>
        <span class="n">zero_point</span><span class="p">)</span></div>

<div class="viewcode-block" id="interpolate"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.interpolate">[docs]</a><span class="k">def</span> <span class="nf">interpolate</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Down/up samples the input to either the given :attr:`size` or the given</span>
<span class="sd">    :attr:`scale_factor`</span>

<span class="sd">    See :func:`torch.nn.functional.interpolate` for implementation details.</span>

<span class="sd">    The input dimensions are interpreted in the form:</span>
<span class="sd">    `mini-batch x channels x [optional depth] x [optional height] x width`.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    .. note:: Only 2D/3D input is supported for quantized inputs</span>

<span class="sd">    .. note:: Only the following modes are supported for the quantized inputs:</span>

<span class="sd">        - `bilinear`</span>
<span class="sd">        - `nearest`</span>

<span class="sd">    Args:</span>
<span class="sd">        input (Tensor): the input tensor</span>
<span class="sd">        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):</span>
<span class="sd">            output spatial size.</span>
<span class="sd">        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.</span>
<span class="sd">        mode (str): algorithm used for upsampling:</span>
<span class="sd">            ``&#39;nearest&#39;`` | ``&#39;bilinear&#39;``</span>
<span class="sd">        align_corners (bool, optional): Geometrically, we consider the pixels of the</span>
<span class="sd">            input and output as squares rather than points.</span>
<span class="sd">            If set to ``True``, the input and output tensors are aligned by the</span>
<span class="sd">            center points of their corner pixels, preserving the values at the corner pixels.</span>
<span class="sd">            If set to ``False``, the input and output tensors are aligned by the corner</span>
<span class="sd">            points of their corner pixels, and the interpolation uses edge value padding</span>
<span class="sd">            for out-of-boundary values, making this operation *independent* of input size</span>
<span class="sd">            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`</span>
<span class="sd">            is ``&#39;bilinear&#39;``.</span>
<span class="sd">            Default: ``False``</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.interpolate&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">scale_factor</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span>
                                           <span class="n">align_corners</span><span class="p">)</span></div>

<div class="viewcode-block" id="linear"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.linear">[docs]</a><span class="k">def</span> <span class="nf">linear</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, Tensor, Optional[Tensor], Optional[float], Optional[int]) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies a linear transformation to the incoming quantized data:</span>
<span class="sd">    :math:`y = xA^T + b`.</span>
<span class="sd">    See :class:`~torch.nn.quantized.Linear`</span>

<span class="sd">    .. note::</span>

<span class="sd">      Current implementation packs weights on every call, which has penalty on performance.</span>
<span class="sd">      If you want to avoid the overhead, use :class:`~torch.nn.quantized.Linear`.</span>

<span class="sd">    Args:</span>
<span class="sd">      input (Tensor): Quantized input of type `torch.quint8`</span>
<span class="sd">      weight (Tensor): Quantized weight of type `torch.qint8`</span>
<span class="sd">      bias (Tensor): None or fp32 bias of type `torch.float`</span>
<span class="sd">      scale (double): output scale. If None, derived from the input scale</span>
<span class="sd">      zero_point (long): output zero point. If None, derived from the input zero_point</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, *, in\_features)` where `*` means any number of</span>
<span class="sd">          additional dimensions</span>
<span class="sd">        - Weight: :math:`(out\_features, in\_features)`</span>
<span class="sd">        - Bias: :math:`(out\_features)`</span>
<span class="sd">        - Output: :math:`(N, *, out\_features)`</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">scale</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">scale</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">q_scale</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">zero_point</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">zero_point</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">q_zero_point</span><span class="p">()</span>
    <span class="n">_packed_params</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear_prepack</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">_packed_params</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">)</span></div>

<div class="viewcode-block" id="max_pool2d"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.max_pool2d">[docs]</a><span class="k">def</span> <span class="nf">max_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
               <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies a 2D max pooling over a quantized input signal composed of</span>
<span class="sd">    several quantized input planes.</span>

<span class="sd">    .. note:: The input quantization parameters are propagated to the output.</span>

<span class="sd">    See :class:`~torch.nn.quantized.MaxPool2d` for details.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">return_indices</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;return_indices is not yet implemented!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">stride</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">stride</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">_List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span>
                                          <span class="n">dilation</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="n">return_indices</span><span class="p">)</span></div>

<div class="viewcode-block" id="relu"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.relu">[docs]</a><span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, bool) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;relu(input, inplace=False) -&gt; Tensor</span>

<span class="sd">    Applies the rectified linear unit function element-wise.</span>
<span class="sd">    See :class:`~torch.nn.quantized.ReLU` for more details.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input</span>
<span class="sd">        inplace: perform the computation inplace</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.relu&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu_</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span></div>

<span class="k">def</span> <span class="nf">leaky_relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
               <span class="n">scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float, bool, float, int) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Quantized version of the.</span>
<span class="sd">    leaky_relu(input, negative_slope=0.01, inplace=False, scale, zero_point) -&gt; Tensor</span>

<span class="sd">    Applies element-wise,</span>
<span class="sd">    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`</span>

<span class="sd">    Args:</span>
<span class="sd">        input: Quaintized input</span>
<span class="sd">        negative_slope: The slope of the negative input</span>
<span class="sd">        inplace: Inplace modification of the input tensor</span>
<span class="sd">        scale, zero_point: Scale and zero point of thhe output tensor.</span>

<span class="sd">    See :class:`~torch.nn.LeakyReLU` for more details.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">zero_point</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">assert</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">,</span> <span class="s2">&quot;Cannot rescale with `inplace`&quot;</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
                                           <span class="n">scale</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">zero_point</span><span class="p">),</span> <span class="nb">input</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">negative_slope</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">output</span>
    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
        <span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">leaky_relu_</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">negative_slope</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">negative_slope</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">result</span>

<span class="k">def</span> <span class="nf">hardtanh</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=-</span><span class="mf">1.</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float, float, bool) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    hardtanh(input, min_val=-1., max_val=1., inplace=False) -&gt; Tensor</span>

<span class="sd">    Applies the quantized HardTanh function element-wise, with scale and</span>
<span class="sd">    zero-point carried over from the input tensor. See :class:`~torch.nn.Hardtanh`</span>
<span class="sd">    for more details.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.hardtanh&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">hardtanh_</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">hardtanh</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">elu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies the quantized ELU function element-wise:</span>

<span class="sd">    .. math::</span>
<span class="sd">        \text{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input</span>
<span class="sd">        alpha: the :math:`\alpha` value for the ELU formulation. Default: 1.0</span>
<span class="sd">        inplace: Inplace modification of the input tensor</span>
<span class="sd">        scale, zero_point: Scale and zero point of the output tensor.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.elu&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="p">(</span><span class="n">scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">zero_point</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Either both or none of (scale, zero_point) must be specified!&quot;</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">zero_point</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">assert</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">,</span> <span class="s2">&quot;Cannot rescale with `inplace`&quot;</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
                                           <span class="n">scale</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">zero_point</span><span class="p">),</span> <span class="nb">input</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">elu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">output</span>
    <span class="k">elif</span> <span class="n">inplace</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">elu_</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">elu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">clamp</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_</span><span class="p">,</span> <span class="n">max_</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;float(input, min_, max_) -&gt; Tensor</span>

<span class="sd">    Applies the clamp function element-wise.</span>
<span class="sd">    See :class:`~torch.nn.quantized.clamp` for more details.</span>

<span class="sd">    Args:</span>
<span class="sd">        input: quantized input</span>
<span class="sd">        min_: minimum value for clamping</span>
<span class="sd">        max_: maximum value for clamping</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input to &#39;quantized.clamp&#39; must be quantized!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">min_</span><span class="p">,</span> <span class="n">max_</span><span class="p">)</span>

<div class="viewcode-block" id="upsample"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.upsample">[docs]</a><span class="k">def</span> <span class="nf">upsample</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Upsamples the input to either the given :attr:`size` or the given</span>
<span class="sd">    :attr:`scale_factor`</span>

<span class="sd">    .. warning::</span>
<span class="sd">        This function is deprecated in favor of</span>
<span class="sd">        :func:`torch.nn.quantized.functional.interpolate`.</span>
<span class="sd">        This is equivalent with ``nn.quantized.functional.interpolate(...)``.</span>

<span class="sd">    See :func:`torch.nn.functional.interpolate` for implementation details.</span>

<span class="sd">    The input dimensions are interpreted in the form:</span>
<span class="sd">    `mini-batch x channels x [optional depth] x [optional height] x width`.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    .. note:: Only 2D input is supported for quantized inputs</span>

<span class="sd">    .. note:: Only the following modes are supported for the quantized inputs:</span>

<span class="sd">        - `bilinear`</span>
<span class="sd">        - `nearest`</span>

<span class="sd">    Args:</span>
<span class="sd">        input (Tensor): quantized input tensor</span>
<span class="sd">        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):</span>
<span class="sd">            output spatial size.</span>
<span class="sd">        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to be an integer.</span>
<span class="sd">        mode (string): algorithm used for upsampling:</span>
<span class="sd">            ``&#39;nearest&#39;`` | ``&#39;bilinear&#39;``</span>
<span class="sd">        align_corners (bool, optional): Geometrically, we consider the pixels of the</span>
<span class="sd">            input and output as squares rather than points.</span>
<span class="sd">            If set to ``True``, the input and output tensors are aligned by the</span>
<span class="sd">            center points of their corner pixels, preserving the values at the corner pixels.</span>
<span class="sd">            If set to ``False``, the input and output tensors are aligned by the corner</span>
<span class="sd">            points of their corner pixels, and the interpolation uses edge value padding</span>
<span class="sd">            for out-of-boundary values, making this operation *independent* of input size</span>
<span class="sd">            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`</span>
<span class="sd">            is ``&#39;bilinear&#39;``.</span>
<span class="sd">            Default: ``False``</span>

<span class="sd">    .. warning::</span>
<span class="sd">        With ``align_corners = True``, the linearly interpolating modes</span>
<span class="sd">        (`bilinear`) don&#39;t proportionally align the</span>
<span class="sd">        output and input pixels, and thus the output values can depend on the</span>
<span class="sd">        input size. This was the default behavior for these modes up to version</span>
<span class="sd">        0.3.1. Since then, the default behavior is ``align_corners = False``.</span>
<span class="sd">        See :class:`~torch.nn.Upsample` for concrete examples on how this</span>
<span class="sd">        affects the outputs.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;nn.quantized.functional.upsample is deprecated. Use nn.quantized.functional.interpolate instead.&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">interpolate</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">scale_factor</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="p">)</span></div>

<div class="viewcode-block" id="upsample_bilinear"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.upsample_bilinear">[docs]</a><span class="k">def</span> <span class="nf">upsample_bilinear</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Upsamples the input, using bilinear upsampling.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        This function is deprecated in favor of</span>
<span class="sd">        :func:`torch.nn.quantized.functional.interpolate`.</span>
<span class="sd">        This is equivalent with</span>
<span class="sd">        ``nn.quantized.functional.interpolate(..., mode=&#39;bilinear&#39;, align_corners=True)``.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    .. note:: Only 2D inputs are supported</span>

<span class="sd">    Args:</span>
<span class="sd">        input (Tensor): quantized input</span>
<span class="sd">        size (int or Tuple[int, int]): output spatial size.</span>
<span class="sd">        scale_factor (int or Tuple[int, int]): multiplier for spatial size</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># DeprecationWarning is ignored by default</span>
    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;nn.quantized.functional.upsample_bilinear is deprecated. Use nn.quantized.functional.interpolate instead.&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">interpolate</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">scale_factor</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;bilinear&#39;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>

<div class="viewcode-block" id="upsample_nearest"><a class="viewcode-back" href="../../../../quantization.html#torch.nn.quantized.functional.upsample_nearest">[docs]</a><span class="k">def</span> <span class="nf">upsample_nearest</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Upsamples the input, using nearest neighbours&#39; pixel values.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        This function is deprecated in favor of</span>
<span class="sd">        :func:`torch.nn.quantized.functional.interpolate`.</span>
<span class="sd">        This is equivalent with ``nn.quantized.functional.interpolate(..., mode=&#39;nearest&#39;)``.</span>

<span class="sd">    .. note:: The input quantization parameters propagate to the output.</span>

<span class="sd">    .. note:: Only 2D inputs are supported</span>

<span class="sd">    Args:</span>
<span class="sd">        input (Tensor): quantized input</span>
<span class="sd">        size (int or Tuple[int, int] or Tuple[int, int, int]): output spatial</span>
<span class="sd">            size.</span>
<span class="sd">        scale_factor (int): multiplier for spatial size. Has to be an integer.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># DeprecationWarning is ignored by default</span>
    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;nn.quantized.functional.upsample_nearest is deprecated. Use nn.quantized.functional.interpolate instead.&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">interpolate</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">scale_factor</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span></div>
</pre></div>

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